Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter

Kristen Johnson, Di Jin, Dan Goldwasser


Abstract
Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse on the microblogging platform, Twitter. Our global probabilistic models show that by combining both lexical features of tweets and network-based behavioral features of Twitter, we are able to increase the average, unsupervised F1 score by 21.52 points over a lexical baseline alone.
Anthology ID:
P17-1069
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
741–752
Language:
URL:
https://www.aclweb.org/anthology/P17-1069
DOI:
10.18653/v1/P17-1069
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1069.pdf
Video:
 https://vimeo.com/234955861